For typical real world objects, it is difficult to ascertain whether an object is “lost” from a set of other objects with which the object is associated, or from which the object should otherwise not be separated. As a simple example, consider a 1000 piece jigsaw puzzle—all of the pieces of the jigsaw puzzle should remain together lest the completed jigsaw puzzle will be incomplete. Without counting the pieces by hand, which is error prone, or going through the exhaustive exercise of piecing together the entire puzzle to see where any hole(s) are, which is time consuming, it is practically impossible to detect a missing puzzle piece from a box full of 999 puzzle pieces that should have 1000.
Even where a person might exhaustively count the 999 puzzle pieces, and deduce that one puzzle piece is missing from the 1000 piece jigsaw puzzle, gaining knowledge of the mere fact that there is a missing puzzle piece is not enough information. For instance, this information alone will not help the person determine what the exact graphic on the missing piece is, when the missing piece was lost, where the missing piece was lost, or to what other pieces the missing piece connects.
Thus, even if the loss of the object is discoverable, more information about the loss would be desirable, such as what the exact nature of the lost object is, when the loss occurred, where the loss occurred, and/or to what other objects the lost object is associated. Today, however, there is no known system that can analyze loss of object(s) in a generalized context aware manner for any pre-associated set of objects and ascertain this kind of information.
There are existing systems that independently detect the presence of items of a set of items within a pre-defined physical space, i.e., systems that can determine whether any of a pre-defined set of items has left the pre-defined physical space. These systems are utilized, for instance, in libraries where detectors are arranged at exits to determine whether any books are leaving the library in an unauthorized manner. These systems have also been applied in the security context, e.g., applied to a safe where a user must explicitly check items in and out of the safe, and the safe operates to interrogate its interior to determine whether the contents are proper. Similar systems have been applied at delivery checkpoints to determine whether any items are missing inside a given truck storage compartment upon arrival of a given truck to a given delivery checkpoint.
However, existing systems are inherently constrained by the 3-D space in which they are implemented. For instance, in the library example, the four corners of the library define the limited 3-D space in which the system operates. Once a book leaves the four corners of the library, the system can determine nothing further about the book. In this sense, such systems are custom tailored solutions for a particular space in that they cannot be used outside of their defined operational space, unless physically moved and installed at a new space (in which case, the system is then constrained to the new space).
Another problem is that existing object detection systems operate on an item-by-item basis without any appreciation of item association. For instance, suppose Volume I and Volume II of an ancient text are taken from a library. While existing systems can determine that Volume I is missing, and independently, that Volume II is missing, there is no certainty as to whether Volume I and II are still together after leaving the library. Once again, no further information about the books can be ascertained. All that is known to such a system is that two independently identified books have left the library in an unauthorized manner.
Accordingly, it would be desirable to provide a generalized framework for any pre-defined set of objects for detecting the disassociation, or loss, of any one or more objects from the pre-defined set. It would be further desirable to provide a general framework that detects such disassociation without being constrained to a pre-defined operating space. It would be still further desirable to provide a framework for detecting disassociation among a group of objects that automatically responds appropriately given the context of the group of objects.
These and other deficiencies in the state of the art of object detection will become apparent upon description of the various exemplary non-limiting embodiments of the invention set forth in more detail below.